This literature review aims to provide an up-to-date analysis of researchers' efforts to understand the key components and the limitations of self-supervised learning.
Section  explains a few basic concepts of self-supervied learning.
Section  discusses how pretext tasks are used in self-supervised learning, and how being invariant to image transformations is better than being covariant to image transformations [1]}.
Then, in section REF , we describe a recent contrastive learning method SimCLR [2]} and in section REF  we introduce attempts to improve contrastive methods by altering its key components [3]}.
Section REF  presents the model bias [4]} in contrastive learning, and is followed by section REF  which discusses possible methods to reduce model bias [5]}, [6]} in current self-supervised learning methods.
